Proposed is the analysis, rational design and implementation of the most efficient metabolic network structure of E. coli. The native network structure is characterized by an inherent redundancy that enables it to carry out metabolic conversions through many different pathway options. In the proposed approach these pathway options are identified through elementary mode analysis which enables determination of a unique set of reaction networksthat determines the functioning of the metabolism. Because all pathway possibilities are known it is then possible to compare them and to rank them according to efficiency where efficiency is measured in terms of the ability to convert available nutrients into desired products. Moreover, because the pathway options are unique it is possible to identify reactions whose knockout eliminates entire specific pathways. The analysis predicted that only six gene knockout mutations are needed to collapse the multiplicity of pathway options into the most efficient one. Such strain containing these mutations has been constructed and preliminary data indicate that it carries the desired metabolic characteristics. A large part of the proposed activities focuses on this strain as it permits the establisment of a much more direct relationship between the cellular genotype and the phenotype as reflected in the cellular metabolism. The methods applied in this study include global transcriptional profiling as well as advanced flowcytometry characterization of cellular dynamics that form the basis for metabolic flux analysis in single cells. The proposed activities are guided by theoretical predictions of metabolic network function. As such the metabolic engineering modifications are carried out on a rational basis. Therefore, the approach taken could form a rigorous way to analyze complex reaction networksand to implement network modifications in an entirely productive manner. The proposed activities will have a profound impact on our fundamental understanding of how metabolic networks function and how this function can be rationally interpreted. This will find immediate use in several areas of biotechnology that aim at redirecting metabolism towards desired substances which may have significant technological and economic impact. As such the proposed activities should be relevant to NSF, NIH, DOE, and USDA.